Search Results (684)

This study aims to develop and demonstrate a low-cost LiDAR-based 3D mapping approach for estimating tree carbon stock in university campuses. Unlike conventional field-based measurements, which are labor-intensive and error-prone, the proposed system integrates a 2D LiDAR sensor with a servo motor and odometry data to generate three-dimensional point clouds of trees. From these data, key biometric parameters such as diameter at breast height (DBH) and total height are automatically extracted and incorporated into species-specific and generalized allometric equations, in line with IPCC 2006/2019 guidelines, to estimate above-ground biomass, below-ground biomass, and total carbon storage. The experimental study is conducted over approximately 70,000 m² of green space at Gazi University, Ankara, where six dominant species have been identified, including Cedrus libani, Pinus nigra, Platanus orientalis, and Ailanthus altissima. Results revealed a total carbon stock of 16.82 t C, corresponding to 61.66 t CO₂eq. Among species, Cedrus libani (29,468.86 kg C) and Ailanthus altissima (13,544.83 kg C) showed the highest contributions, while Picea orientalis accounted for the lowest. The findings confirm that the proposed system offers a reliable, portable, cost-effective alternative to professional LiDAR scanners. This approach supports sustainable campus management and highlights the broader applicability of low-cost LiDAR technologies for urban carbon accounting and climate change mitigation strategies. Full article

Accurate estimation of commercial tree height is essential for volumetric predictions in forest management plans, particularly in Amazonian forests with high species diversity. We assessed two predictive approaches for estimating commercial height, using the sum of actual commercial log lengths as the reference metric. The dataset comprised 1745 harvested trees from Annual Production Unit 8 in the Tapajós National Forest. Three commercial volume groups dominated the structural gradient: 46.1% of the trees Group 1 (<6 m³), 36.7% into Group 2 (6–10 m³), and 17.2% into Group 3 (≥10 m³). The Linear Mixed-Effects Model included diameter at breast height (DBH) as a fixed effect and species as a random effect, whereas the Random Forest model used DBH and species as predictors. The mixed-effects model achieved higher accuracy (r = 0.77; RMSE = 2.95 m), while the Random Forest model performed slightly worse (r = 0.73; RMSE = 3.10 m). Species with greater commercial heights exerted a strong influence on both modelling approaches. Principal Component Analysis revealed structural separation among the three volume groups, driven by DBH, commercial height, number of logs, and log volume. The mixed-effects model provided effective framework for predicting commercial height in heterogeneous tropical forests. Full article

The species, quantity, and tree diameter at breast height (DBH) are important indicators for assessing species distribution, individual growth status, and overall health in the forest. The existing tree information collection mainly relies on manual labor, which results in low efficiency and high labor intensity. To address these issues, we propose a method for tree species identification and diameter estimation by combining deep learning algorithms with binocular vision. First, an image acquisition platform is designed and integrated with a weeding machine to capture images during weeding operation. Images of seven types of trees are captured to develop a dataset. Second, a tree species identification model is established based on the YOLOv8n network, achieving 98.5% accuracy, 99.0% recall, and 99.2% mAP. Then, an improved YOLOv8n-seg model is proposed. It simplifies the network by introducing VanillaBlock in the backbone. FasterNet with a CCFM structure is added at the neck to enhance the model’s multi-scale expression capability. The mIoU of the improved model is 93.7%. Finally, the improved YOLOv8n-seg model is combined with binocular vision. After obtaining the segmentation mask of the tree, the spatial position of the two measurement points is calculated, allowing for the measurement of tree diameter. Verification experiments show that the average error for tree diameter ranges from 4.40~6.40 mm, and the proposed error compensation method can reduce diameter errors. This study provides a theoretical foundation and technical support for intelligent collection of tree information. Full article

Accurate and efficient forest inventory methods are crucial for monitoring forest ecosystems, assessing carbon stocks, and supporting sustainable forest management. Traditional field-based techniques, which rely on manual measurements such as diameter at breast height (DBH) and tree height (TH), remain labour-intensive and time-consuming. In this study, we introduce and validate a fully open-source, low-cost terrestrial laser scanning system (LCA-TLS) built from commercially available components and based on the Livox Avia sensor. With a total cost of €2050, the system responds to recent technological developments that have significantly reduced hardware expenses while retaining high data quality. This trend has created new opportunities for broadening access to high-resolution 3D data in ecological research. The performance of the LCA-TLS was assessed under controlled and field conditions and benchmarked against three reference devices: the RIEGL VZ-1000 terrestrial laser scanner, the Stonex X120GO handheld mobile laser scanner, and the iPhone 15 Pro Max structured-light device. The LCA-TLS achieved high accuracy for estimating DBH (RMSE: 1.50 cm) and TH (RMSE: 0.99 m), outperforming the iPhone and yielding results statistically comparable to the Stonex X120GO (DBH RMSE: 1.32 cm; p > 0.05), despite the latter being roughly ten times more expensive. While the RIEGL system produced the most accurate measurements, its cost exceeded that of the LCA-TLS by a factor of about 30. The hardware design, control software, and processing workflow of the LCA-TLS are fully open-source, allowing users worldwide to build, modify, and apply the system with minimal resources. The proposed solution thus represents a practical, cost-effective, and accessible alternative for 3D forest inventory and LiDAR-based ecosystem monitoring. Full article

Dalbergia odorifera T. Chen possesses significant aromatic, medicinal, and timber value, yet its wild populations are critically endangered due to habitat degradation. Breeding programs are urgently needed to address resource shortages, but the suitability of large-scale plantations as alternative genetic resources remains unverified. This study systematically evaluated the genetic diversity of 380 individuals from five populations using 24 polymorphic SSR markers, identifying 278 alleles. The results demonstrated a moderate level of genetic diversity in plantation populations, comparable to wild resources. Additionally, nine phenotypic traits were measured in 70 individuals. Correlation analysis revealed that the heartwood ratio (HWR) was significantly positively correlated with diameter at breast height (DBH) and ground diameter (GD) (p ≤ 0.05). Our association analysis, based on general linear (GLM) and mixed linear models (MLM), revealed two key findings: one locus (96c-345) was significantly associated with diameter traits, and four loci (34a-241, S03-265, JXHT097-252, JXHT136-270) were strongly linked to the HWR (p ≤ 0.01). This research provides initial evidence that plantations are viable substitutes for wild germplasm and establishes a foundation for marker-assisted breeding in this valuable species. Full article

Subtropical deciduous broad-leaved forests in eastern China form a key ecotone between temperate and subtropical biomes, yet their vegetation–environment relationships remain insufficiently understood. This study examined community structure, species diversity, and their associations with topographic and soil variables in a 25 ha forest dynamics plot in the Lushan Mountains. All woody plants with a diameter at breast height (DBH) ≥ 1 cm were surveyed, and detailed topographic attributes and soil physicochemical properties were measured. Community structure showed strong linkages with species diversity: tree-layer structural characteristics were generally negatively correlated with diversity, whereas in the shrub layer, density was negatively but height and DBH were positively correlated with diversity. Species diversity in the two layers was positively associated, while tree-layer structure was negatively related to shrub-layer diversity. Among topographic factors, altitude and the topographic solar radiation aspect index (TRASP) exerted the strongest influences on soil properties, with altitude negatively correlated with soil pH and available nutrients but positively correlated with C:N, C:P, and total carbon, and TRASP showing negative correlations with most nutrients except total phosphorus. Redundancy analysis revealed that topographic heterogeneity and soil conditions jointly shaped community structure and species diversity, with soil C:N ratio, altitude, pH, total phosphorus, and total carbon emerging as key drivers. These findings demonstrate that areas with high plant diversity do not always correspond to high soil nutrient content and underscore the importance of integrating both topographic and edaphic factors into biodiversity conservation and forest management in subtropical deciduous broad-leaved forests. Full article

Eucalyptus urophylla is a core tree species for short-rotation industrial timber plantations in South and Southwest China. However, the dynamic correlation rules of its growth traits during the full rotation period remain unclear, and the theoretical research on early selection is insufficient. In this study, 12 pure E. urophylla clones (including U6 and MLA as controls) were used as plant materials. Based on the data of tree height (H), diameter at breast height (DBH, D), and individual tree volume (V) from 0.5 to 7.5 years old, the correlation rules of early and late growth traits were explored, core predictive traits were screened, and the optimal selection age was determined through rank correlation, phenotypic and genetic correlation analyses, combined with regression modeling and selection efficiency calculation. Early selection of E. urophylla clones was feasible: after 3.5 years, the early–late phenotypic and genetic correlation coefficients of H, D, and V all reached significant or highly significant levels, and the genetic correlation coefficients were greater than the phenotypic ones, indicating that genetic factors dominated trait correlations with little environmental interference. All five established early selection regression models passed the highly significant test. Among them, the models of D-early versus D-late, V-early versus V-late, and D-early versus V-late had the highest coefficients of determination (0.9293–0.9385), making them the optimal selection traits; the models of H-early versus H-late and H-early versus V-late had lower coefficients of determination (0.8010–0.8364) due to errors in height measurement. The best selection effect was achieved within 1/2–2/3 of the rotation period: for a 6-year rotation period (pulpwood), the optimal selection age was 3.5 years old (annual efficiency 1.318); for an 8-year rotation period (medium-diameter timber), it was 4.5 years old (annual efficiency 1.345); and for a 12-year rotation period (large-diameter timber), it was 6.5 years old (annual efficiency 1.379). This study not only fills the theoretical gap in early selection of E. urophylla during the full rotation period but also constructs an integrated early selection technology system of “trait screening—model prediction—age determination”. It provides key support for shortening the breeding cycle of E. urophylla and achieving precise control of breeding costs and offers important references for early selection research on fast-growing broad-leaved tree species worldwide. Full article

Pruning is a regular and essential urban tree maintenance practice aimed at sustaining overall health, ecosystem services, and public safety. However, knowledge of post-pruning recovery dynamics remains limited, which in turn hinders accurate assessments of growth and ecological functions. To address this, we examined recovery dynamics of Acacia auriculiformis, a common urban species. Tree height and crown radius were recorded monthly for 12 months after pruning. Trees were classified into two size groups based on diameter at breast height (DBH, trunk diameter measured at 1.3 m above ground): medium (DBH < 45 cm) and large (DBH ≥ 45 cm). A generalized linear mixed model (GLMM), appropriate for repeated measures and non-normal data, was fitted using a Tweedie distribution and a log-link function to model the recovery pattern. Results showed continuous growth over time, with medium-sized trees presenting significantly higher crown radius growth than large trees (p = 0.006), while height growth did not differ (p = 0.788). The best model for height included time (AIC = −846.4), whereas crown recovery was best modelled by time and size class (AIC = −1586.6). These findings demonstrate that, in this study, medium-sized A. auriculiformis generally recover faster, especially in crown expansion. This exploratory study suggests that tree size may influence post-pruning recovery and can provide a reference for subsequent differentiated management studies. The morphological modeling further provides preliminary quantitative evidence for annual recovery dynamics in urban A. auriculiformis. Full article

Forest measurements are essential for monitoring stand dynamics and long-term trends. Errors in tree measurement can seriously affect the outcomes of a forest inventory. This study investigates measurement errors from successive measurements on permanent concentric circular plots based on data from 74 plots in Dinaric uneven-aged mixed fir–beech stands. Tree data errors were detected and corrected. Diameter increment was calculated as a difference in DBH from two successive inventories, and linear regression models were developed based on original and corrected data. Measurement errors were identified in 2.57% of trees, some having a substantial impact on tree volume. Volume discrepancies between original and corrected data were generally minor, where 93.2% of plots in the first and 70.3% in the second inventory required no corrections and volume differences in the overall levels were negligible and statistically non-significant: 0.30 m³/ha in the first inventory (p = 0.550) and 0.05 m³/ha in the second (p = 0.974). Although diameter increment models with original and corrected data differed significantly, model choice resulted in minimal impact on volume increment. Since omitting erroneous measurement data would lead to volume underestimation, data correction is preferable. However, when modeling tree increment, excluding incorrect or doubtful data remains a practical and acceptable approach. Full article

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices. Full article

Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and conducting forest inventories due to its complex stand structure and overlapping crowns. To determine the most effective ITS approach for P. euphratica, we benchmarked six commonly used tree segmentation approaches for terrestrial laser scanning (TLS) data: canopy height model segmentation (CHMS), point cloud segmentation (PCS), comparative shortest-path algorithm (CSP), stem location seed point segmentation (SPS), deep-learning trunk-based segmentation (TBS), and leaf–wood separation-based segmentation (LWS). All methods followed a unified preprocessing and tuning protocol. We evaluated these methods based on tree-count accuracy, crown delineation, and structural attributes such as tree height (H), diameter at breast height (DBH), and crown diameter (CD). The results indicated that the TBS and LWS methods performed the best, achieving a mean tree-count accuracy of 98%, while the CHMS method averaged only 46%. These two methods provide the basic branch structure within the tree crown, reducing the likelihood of incorrect segmentation. Validation against field-measured values for H, DBH, and CD showed that both the TBS and LWS methods achieved accuracies exceeding 80% (RMSE = 0.8 m), 86% (RMSE = 0.02 m), and 73% (RMSE = 0.7 m), respectively. For TLS data in P. euphratica desert riparian forests, these two methods provide the most reliable results, facilitating rapid plot-scale inventory and monitoring. These findings establish a practical basis for conducting high-accuracy inventories of Euphrates poplar desert riparian forests. Full article

The ecological strategies of understory shrubs are critical for maintaining the structure and function of forest understory vegetation. Understanding the assembly mechanisms of these shrub communities is a central issue in modern ecology. To address this, our study was conducted in the typical red soil regions of Jiangxi, China, focusing on secondary forests (including both broadleaved and coniferous types) of similar stand age. We aimed to assess the effects of various environmental factors—such as soil pH, total nitrogen content, bulk density, and understory temperature—along with tree-layer characteristics—including canopy closure, tree species richness, and diameter at breast height (DBH)—on the species composition, functional traits, and phylogenetic structure of the shrub layer. Results showed: One-way ANOVA revealed significant differences in functional traits between the two forest types. Specifically, leaf thickness, specific leaf area, and chlorophyll content were significantly higher in the coniferous forest, whereas leaf dry matter content was significantly lower compared to the broadleaved forest (p < 0.05). These results suggest that understory shrubs in the coniferous forest primarily adopt a resource-conservative strategy, while those in the broadleaved forest exhibit a resource-acquisitive strategy. Phylogenetic analysis further revealed that the phylogenetic diversity (PD) of coniferous forests was significantly lower than that of broadleaved forests (p < 0.05). The phylogenetic structure in coniferous forests showed a more clustered pattern (NTI > 0, NRI > 0), suggesting stronger environmental filtering. Diversity index analysis showed that the Chao1 index indicated a richer potential species pool in broadleaved forests (p < 0.05), while species distribution was more even in coniferous forests (p < 0.05). Random Forest model analysis identified the diameter at breast height (DBH) of trees as the most critical negative driver, while soil pH was the primary positive driver. Redundancy Analysis (RDA) confirmed that the community structure in coniferous forests was mainly driven by biotic competition pressure represented by DBH, whereas the structure in broadleaved forests was more closely associated with abiotic factors like soil total nitrogen and pH (R² = 0.29, p < 0.05). These environmental drivers, through strong environmental filtering, collectively resulted in a phylogenetically clustered pattern of shrub communities in both forest types. This study demonstrates that the assembly of understory shrub communities is a complex, multi-level process co-regulated by multiple factors, shaped by both the biotic pressure from the overstory structure and abiotic filtering from the soil environment. This finding deepens our understanding of the rules governing community assembly in forest ecosystems. Full article

This study aimed to develop a thinning tree selection system by applying genetic algorithms based on precisely estimated tree-level forest structural parameters derived from LiDAR data. Conventional thinning tree selection methods have limitations due to their dependence on subjective judgement and field experience of operators, resulting in inconsistency and variations according to skill levels. To address these issues, tree positions, diameters at breast height (DBH), and tree heights were extracted by integrating terrestrial laser scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS) data, forming a Multi-Platform LiDAR dataset. The derived DBH and Hegyi competition index were utilized as indicators for thinning tree selection. Optimization of tree selection was performed using a genetic algorithm, with an objective function designed to maximize the average DBH and minimize the average competition index of the remaining trees, and the system’s performance was compared with results obtained by forestry experts. The results showed that tree detection accuracy exceeded 99%, DBH estimation exhibited an RMSE of 0.74 cm, and tree height estimation showed an RMSE of approximately 2 m, demonstrating the construction of precise forest structural parameters. Compared to expert driven selection, the Genetic Algorithm-based thinning system produced a higher average DBH (30.06 cm vs. 29.26 cm) and a lower Hegyi competition index (1.31 vs. 1.41) under Scenario 3. This indicates superior performance in competition alleviation and growing space allocation among individual trees. Spatial statistical analysis revealed that while expert selection maintained the existing spatial clustering pattern of stand structure (Global Moran’s I = 0.16), the machine learning system achieved an almost random distribution (Global Moran’s I = −0.04) under Scenario 3. This study demonstrates the potential of overcoming the limitations of conventional thinning practices dependent on subjective judgement by introducing an objective, consistent, data-driven quantitative decision support system for precision forest management. Full article

Understory vegetation diversity is the key indicator of ecological outcomes in the close-to-nature transformation of plantations, with its composition revealing successional dynamics and ecosystem functionality. In response to China’s “Green and Beautiful Guangdong” Initiative, enhancing the ecological quality of plantations has been established as a critical objective for sustainable forest management. This study assessed the understory vegetation in four representative transformed plantations in Guangdong Province, China, using Multi-Response Permutation Procedure (MRPP), Indicator Species Analysis (ISA), Detrended Correspondence Analysis (DCA), and Redundancy Analysis (RDA). The results showed that: (1) Species richness was highest in the Eucalyptus L’Hér plantation (102 species), followed by Pinus massoniana Lamb (94), Acacia mangium Willd (92), and Litchi chinensis Soon plantations (85). (2) MRPP analysis revealed significant differences in species composition among plantation types (A = 0.149, p < 0.001). ISA identified 5, 7, 3, and 5 indicator species for each type, respectively, predominantly light-demanding pioneers such as Dicranopteris dichotoma (Thunb.) Bernh and Microstegium vagans (Nees ex Steud.) A. Camus. (3) DCA ordination showed clear compositional segregation among the understory communities of Eucalyptus, Pinus massoniana, and Acacia mangium plantations, whereas the Litchi chinensis plantation exhibited substantial overlap with others. RDA further demonstrated a significant negative correlation between mean diameter at breast height (DBH) and understory diversity (p < 0.01) across all plantations except Litchi chinensis. These findings offer a quantitative basis for tailored management strategies. We recommend structural adjustments through target-tree thinning to optimize light availability by regulating DBH, combined with interplanting native understory species. This integrated approach can enhance structural heterogeneity and promote more effective and sustainable plantation restoration. Full article

Urban mediterranean forests are key components of urban ecosystems. Accurate, high-resolution data on forest structural attributes are essential for effective management. This study evaluates the efficiency of unmanned laser scanning systems (ULS) and terrestrial LiDAR (TLS) in deriving key tree attributes, diameter at breast height (DBH) and tree height, within a small urban park in Zadar, Croatia. Accuracy assessment of the ULS and TLS-derived DBH was conducted based on traditional ground-based measurement (TGBM) data. For ULS, an automatic Spatix workflow was applied that classified points into a Tree class, segmented trees using trunk-based logic, and estimated DBH by fitting a circle to a 1.3 m slice; tree height was computed from the ground-normalized cloud with the Output Tree Cells tool. A semi-automatic CloudCompare/ArcMap workflow used CSF ground filtering, Connected Components segmentation, extraction of a 10 cm slice, manual trunk vectorization, and DBH calculation via Minimum Bounding Geometry. TLS scans, processed in FARO SCENE, were then analyzed in Spatix using the same automatic trunk-fitting procedure to derive DBH and height. Accuracy for DBH was evaluated against TGBM; comparative performance was summarized with standard error metrics, while ULS and TLS tree heights were compared using Concordance Correlation Coefficient (CCC) and Bland–Altman statistics. Results indicate that the semi-automatic approach outperformed the automatic approach in deriving DBH. TLS-derived DBH values demonstrated higher consistency and agreement with TGBM, as evidenced by their strong linear correlation, minimal bias, and narrow residual spread, while ULS exhibited greater variability and systematic deviation. Tree height comparisons between ULS and TLS revealed that ULS consistently produced slightly higher and more uniform measurements. This study highlights limitations in the evaluated techniques and proposes a hybrid approach combining ULS scanning with personal laser scanning (PLS) systems to enhance data accuracy in urban forest assessments. Full article